3Dカードボックスモデルと新規決定論的リサンプリングを用いたパーティクルフィルタによる単眼人体追跡
Particle Filter Based Monocular Human Tracking with a 3D Cardbox Model and a Novel Deterministic Resampling Strategy
単眼シルエットマッチングと階層的パーティクルフィルタで上半身動作を追跡する手法を提案し、新規の決定論的リサンプリング戦略が従来法より優れることと3Dカードボックスモデルの有効性を実験で示した。
著者: Ziyuan Liu, Dongheui Lee, Wolfgang Sepp
分類: cs.CV, cs.RO
原文アブストラクト
The challenge of markerless human motion tracking is the high dimensionality of the search space. Thus, efficient exploration in the search space is of great significance. In this paper, a motion capturing algorithm is proposed for upper body motion tracking. The proposed system tracks human motion based on monocular silhouette-matching, and it is built on the top of a hierarchical particle filter, within which a novel deterministic resampling strategy (DRS) is applied. The proposed system is evaluated quantitatively with the ground truth data measured by an inertial sensor system. In addition, we compare the DRS with the stratified resampling strategy (SRS). It is shown in experiments that DRS outperforms SRS with the same amount of particles. Moreover, a new 3D articulated human upper body model with the name 3D cardbox model is created and is proven to work successfully for motion tracking. Experiments show that the proposed system can robustly track upper body motion without self-occlusion. Motions towards the camera can also be well tracked.
